The key difference of the particle filter utility between our method and the line of works above lies in the objective. Taking the camera tracking in SLAM as an example, those previous works (e.g. [1]}) optimize for sequential state (pose) estimation throughout a sequence of frames via maximizing observation likelihood. On the other hand, our work optimizes the camera pose of a single (current) frame. In short, the sequential importance sampling is across different frames in [1]} and across increasing iteration steps for the pose of a single frame in our work. Figure REF  contrasts the two different problems with their underlying probabilistic graphical models.
